🚀 MemOS Local Plugin 2.0 is LIVE — 1 memory engine, all Agents fully supported. @NousResearch's Hermes and @openclaw both run on the same core from NOW on. Add another Agent later? It's a thin adapter, not a fork One severe issue kept coming back from users: "An Agent can finish tasks, but can we trust what it learned?" MemOS Local Plugin 2.0 is our answer: Execution as learning. Not just storing chats, but turning each task step into reusable memory.

Most AI Agents don't actually learn from your tasks — they just remember chat logs. MemOS Local Plugin 2.0 ships with 6 things that change this for Hermes and OpenClaw: ☞ Each task step gets captured, scored, and turned into a reusable artifact — not transient context ☞ Dual feedback loops: environment outcomes per step + your verdict on the whole task ☞ 4-layer memory: traces → experience → domain cognition → Skills (with reliability + lifecycle) ☞ 3-tier retrieval: Skills for skeleton, Traces for edge cases, Domain cognition for planning ☞ Memory travels across Agents — teach Hermes today, use it in OpenClaw tomorrow ☞ Full Viewer: every trace, score, experience, and skill is point-and-clickable Open source & Free🆓 npm: @memtensor/memos-local-plugin

Try it on a real task today — curious to hear what your Agent learns first. 📄 Docs: memos-docs.openmem.net/openclaw/herme… 🌟 GitHub: github.com/MemTensor/MemOS #AIAgents #LLM #OpenSource #AgentMemory